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Abstract
The vegetation health index (VHI) is a widely utilized remote-sensing-based index for monitoring agricultural drought on the regional or global scale. However, the validity of VHI as a drought detection tool relies on the assumption that the normalized difference vegetation index (NDVI) and land-surface temperature (Ts) at a given pixel will vary inversely over time. This assumption may introduce large uncertainties in VHI for drought monitoring over areas with complex landforms, such as China. In order to monitor agricultural drought over the whole of China, a new drought detection index is suggested in this article, termed the vegetation drought index (VDI). VDI is developed from the classical VHI by substituting NDVI and Ts with the normalized difference water index (NDWI) and day–night Ts difference (∆Ts), respectively. Terra Moderate Resolution Imaging Spectroradiometer (MODIS) MOD11C3 and MOD13C2 products from 2001 to 2011, monthly precipitation data from 1970 to 2010, and yearly winter wheat yield data from 2000 to 2012 were utilized to evaluate VDI. Results indicated that (1) many areas in China show a positive correlation between NDVI and Ts, especially in the cold season, whereas most areas have a negative correlation between NDWI and ∆Ts; (2) VDI has a significant linear correlation with VHI in areas and periods where the NDVI–Ts correlation and NDWI–∆Ts correlation are both negative; (3) VDI presents a significant correlation with 3 and 6 month standardized precipitation indices, which is comparable to VHI; and (4) VDI has a significant correlation with normalized crop yield, and is better than VHI. As an example, the extreme drought event over southwestern China from winter 2009 to spring 2010 was successfully explored by VDI. It is concluded that the new index, VDI, has the potential to monitor agricultural drought over the whole of China, including areas and periods where the NDVI–Ts correlation is non-negative.
Acknowledgements
The authors would like to thank Mengjie Wang and Professor Daoyi Gong for help in processing the precipitation data. Thanks are also expressed to NASA and the website of China Arid Meteorology for providing the MODIS and precipitation data, respectively.
Funding
This work was supported by the Key Technologies R&D Programme (2012BAJ23B05), Project for Science & Technology for Beijing Excellent Doctoral Dissertation Instructor (20131002702), International Scientific and Technological Cooperation Programme (2010DFA32920), Programme for New Century Excellent Talents in University (NCET-12-0057), and State Key Laboratory of Earth Surface Processes and Resource Ecology (2013-ZY-09).